AI design & governance
Put AI to work in your practice — with the data boundary designed in before the first prompt.
Secure AI Engineering is how RedLab Security helps you adopt or build AI the way a regulated business has to: we decide with you which data may ever reach a model, where it's processed, how long it's kept, who can use it, and how the output is checked — then hand your IT provider or developer a reviewed design to build from, and verify the result. Every control lands in your compliance documentation, not just in a slide.
AI Use Assessment
Vendor may not train on your prompts or files
Current
Verified Oct 18
Vendor Terms
A person reviews output before it's filed or sent
Gap
Verified Oct 20
Output Handling
Patient records redacted before they reach any model
Current
Verified Oct 22
Data Boundary
Every AI use gets a data boundary
— before it goes live, not after a leak.
Designed around your data
A buildable design, not a warning
Mapped to the rules you already answer to
Built on recognized guidance
The design starts with what the model is allowed to see — patient, client, card, or privileged data — not with the vendor's defaults.
Your IT provider or developer receives an architecture and control spec they can quote and build from — then we verify it.
HIPAA, the FTC Safeguards Rule, ABA guidance on generative AI, your SOC 2 path — the same obligations, applied to AI.
Grounded in the NIST AI Risk Management Framework and the OWASP Top 10 for LLM applications — not opinions about AI.
Two ways this engagement starts
Most practices don't adopt AI by decision — it arrives inside the tools they already pay for, or on a staff member's phone. Either way, the question we answer is identical: what data touches a model, under what terms, and who checks the result.
You want to build or adopt an AI solution
Path A - Design
An intake or scheduling assistant, document summarization, a knowledge assistant for your own files, an AI feature in your product. We design how it should work before anyone buys or builds it:
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Which model or vendor fits — and the agreement terms it must carry
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Where processing happens, and what may never leave your environment
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Who can use it, through which sign-in, with what logging
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Where a person reviews output before it's acted on
AI is already in use and nobody drew the line
Path B - Govern
The AI note-taker a clinician trialed, the assistant switched on inside your email and documents suite, staff pasting client files into a free chat tool. We inventory what's actually running and close the gaps:
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Every AI tool in use, what data it touches, and under whose account
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Vendor terms checked: training on your data, retention, breach duties, BAA where required
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A written AI acceptable-use policy your staff acknowledge
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Settings handed to your IT provider to restrict what isn't approved
The deliverable your builder works from
Every Secure AI Engineering design is written for the person who has to implement it — your IT provider, your developer, or the vendor's onboarding team. Each control names the setting or pattern, where it lives, and how we'll verify it. Nothing goes out as a generic AI checklist.
Compliance Status
Data classification & permitted inputs
Which data categories may enter the system, which are redacted first, and which are excluded outright — with the practical rule staff follow.
Vendor & model selection, with required terms
The chosen service and why; the agreement terms it must carry (BAA or data-processing terms, no-training, retention, breach notice); what to decline if a vendor won't sign.
Identity, access & logging
Who may use it and through which sign-in; least-privilege scopes for every integration; what gets logged and where. Verify: an unapproved account or scope is denied.
Output handling & human review
Where a person approves output before it's acted on, where output is stored, and how clients or patients are told AI is in use when required.
Threat model for this use case
Prompt injection through documents or email, data leaking through over-permissioned connectors, poisoned reference files, and the specific defenses — drawn from the OWASP Top 10 for LLM applications.
Reviewed by RedLab Security